Oleg Zabluda's blog
Monday, October 10, 2016
 
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In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. (This term should be distinguished from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.)
[...]
Linear regression models are often fitted using the least squares approach [...] or by minimizing a penalized version of the least squares loss function as in ridge regression (L2-norm penalty) and lasso (L1-norm penalty).
"""
https://en.wikipedia.org/wiki/Linear_regression
https://en.wikipedia.org/wiki/Linear_regression

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